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Record W4402278926 · doi:10.3390/molecules29174213

Amphetamine-like Deferiprone and Clioquinol Derivatives as Iron Chelating Agents

2024· article· en· W4402278926 on OpenAlexaff
Mahmoud El Safadi, Katie A. Wilson, Indigo J. Strudwicke, Megan L. O’Mara, Mohan Bhadbhade, Tristan Rawling, Andrew M. McDonagh

Bibliographic record

VenueMolecules · 2024
Typearticle
Languageen
FieldMedicine
TopicMetal complexes synthesis and properties
Canadian institutionsMemorial University of Newfoundland
FundersNational Cancer InstituteAustralian GovernmentNational Computational Infrastructure
KeywordsDeferiproneChemistryDopaminergicChelationDopamineClioquinolOxidative stressAmphetaminePharmacologyBiochemistryOrganic chemistryNeuroscienceMedicineBiology

Abstract

fetched live from OpenAlex

The accumulation of iron in dopaminergic neurons can cause oxidative stress and dopaminergic neuron degeneration. Iron chelation therapy may reduce dopaminergic neurodegeneration, but chelators should be targeted towards dopaminergic cells. In this work, two series of compounds based on 8-hydroxyquinoline and deferiprone, iron chelators that have amphetamine-like structures, have been designed, synthesized and characterized. Each of these compounds chelated iron ions in aqueous solution. The hydroxyquinoline-based compounds exhibited stronger iron-binding constants than those of the deferiprone derivatives. The hydroxyquinoline-based compounds also exhibited greater free radical scavenging activities compared to the deferiprone derivatives. Molecular dynamics simulations showed that the hydroxyquinoline-based compounds generally bound well within human dopamine transporter cavities. Thus, these compounds are excellent candidates for future exploration as drugs against diseases that are affected by iron-induced dopaminergic neuron damage, such as Parkinson's disease.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.043
GPT teacher head0.296
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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